For nearly sixty years, radio detection and ranging (radar) has provided a method for determining the direction and speed of distant objects, for example, airplanes. The method includes transmitting electromagnetic waves toward the distant object, the electromagnetic waves reflecting therefrom, receiving the reflected electromagnetic waves, and processing the reflected electromagnetic waves to determine information about the object. Over the years, the applications for radar have expanded greatly and include, for example, meteorological detection of precipitation, measuring ocean surface waves, air traffic control, and highway speed control devices.
Another approach to remote detection and ranging is light detection and ranging (LIDAR). LIDAR operates in a similar manner to radar but uses light to gather information about the distant object. As will be appreciated by those skilled in the art, LIDAR uses much smaller wavelengths than radar, providing for better resolution.
As demands on LIDAR/radar have grown over the years, applications are using greater resolution and sensitivity. To achieve these goals, the receiver of the typical LIDAR/radar device has become more sensitive to signals. Indeed, for example, in some LIDAR applications, the receiver may comprise a Geiger mode avalanche photodiode, which is capable of detecting individual photons. The LIDAR/radar devices also allow imaging through visual obscurations, such as, clouds and foliage, for example.
A potential drawback to this increased sensitivity is that the receiver may be more susceptible to noise interference, for example, internal system based noise and ambient noise. More particularly, in applications where the desired received signal is low strength, the noise may appear indecipherable from the signal. A typical approach to this drawback is noise filtering, i.e. the process or removing the noise elements from the received signal. For example, the received signal may be filtered to remove all signal elements below a threshold in signal strength. Although this approach may be helpful for applications where the received signal has a high signal-to-noise ratio (SNR), this approach may not be desirable in applications with low SNR since such filtering may remove the desired signal along with any noise.
Another approach to noise filtering is voxel coincidence processing (VCP). The VCP method may include creating a three dimensional histogram, maintaining a threshold count value for each bin, and removing low level signals in highly obscured environments. Another approach to noise filtering is multi-peak range coincidence processing (MPRCP). The MPRCP method may comprise providing a histogram in angle-angle-range space, performing low-pass filtering of range profiles, and converting peaks to (x,y,z) observations. A potential drawback to MPRCP methods may include, for example, the undesired removal of low level signals in highly obscured environments.
An approach to noise filtering in LIDAR applications is disclosed in U.S. Pat. No. 7,304,645 to Blask et al., assigned to the Harris Corporation of Melbourne, Fla., the assignee of the present application. This method may include loading LIDAR point data into a three-dimensional voxel array as a plurality of components, determining connected components in the array, determining a size for each component and a hit count of occupied voxels, and determining whether each occupied voxel is to be written to an output file. The occupied voxels are written to the output file according to a set of criteria based on statistics for determining when a voxel represents a light pulse reflected by a physical object.